Armillaria genus represents one of the most common causes of chronic root rot disease in woody plants. Prompt recognition of diseased plants is crucial to control the pathogen. However, the current disease detection methods are limited at a field scale. Therefore, an alternative approach is needed. In this study, we investigated the potential of hyperspectral techniques to identify fungi-infected vs. healthy plants of Vitis vinifera. We used the hyperspectral imaging sensor Specim-IQ to acquire leaves’ reflectance data of the Teroldego Rotaliano grapevine cultivar. We analyzed three different groups of plants: healthy, asymptomatic, and diseased. Highly significant differences were found in the near-infrared (NIR) spectral region with a decreasing pattern from healthy to diseased plants attributable to the leaf mesophyll changes. Asymptomatic plants emerged from the other groups due to a lower reflectance in the red edge spectrum (around 705 nm), ascribable to an accumulation of secondary metabolites involved in plant defense strategies. Further significant differences were observed in the wavelengths close to 550 nm in diseased vs. asymptomatic plants. We evaluated several machine learning paradigms to differentiate the plant groups. The Naïve Bayes (NB) algorithm, combined with the most discriminant variables among vegetation indices and spectral narrow bands, provided the best results with an overall accuracy of 90% and 75% in healthy vs. diseased and healthy vs. asymptomatic plants, respectively. To our knowledge, this study represents the first report on the possibility of using hyperspectral data for root rot disease diagnosis in woody plants. Although further validation studies are required, it appears that the spectral reflectance technique, possibly implemented on unmanned aerial vehicles (UAVs), could be a promising tool for a cost-effective, non-invasive method of Armillaria disease diagnosis and mapping in-field, contributing to a significant step forward in precision viticulture.

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使用高光谱反射早期识别根腐病：以葡萄藤/蜜环菌为例

蜜环菌该属是木本植物慢性根腐病的最常见原因之一。及时识别患病植物对于控制病原体至关重要。然而，目前的疾病检测方法仅限于现场规模。因此，需要一种替代方法。在这项研究中，我们研究了高光谱技术在识别受真菌感染和健康的葡萄树植物方面的潜力。我们使用高光谱成像传感器 Specim-IQ 来获取 Teroldego Rotaliano 葡萄品种的叶子反射数据。我们分析了三组不同的植物：健康、无症状和患病。在近红外 (NIR) 光谱区域发现了非常显着的差异，由于叶肉的变化，从健康植物到患病植物的模式逐渐减少。由于红色边缘光谱（约 705 nm）中的反射率较低，无症状植物从其他组中出现，这归因于参与植物防御策略的次生代谢物的积累。在患病植物与无症状植物中，在接近 550 nm 的波长中观察到了进一步的显着差异。我们评估了几种机器学习范式来区分植物群。朴素贝叶斯 (NB) 算法结合植被指数和光谱窄带中最具判别性的变量，在健康与患病植物和健康与无症状植物中的总体准确度分别为 90% 和 75%。据我们所知，这项研究是关于使用高光谱数据进行木本植物根腐病诊断的可能性的第一份报告。蜜环菌病诊断和现场测绘，为精准葡萄栽培向前迈出了重要一步。